Artificial Neural Networks/Radial Basis Function Networks

In a radial basis function (RBF) networks are neural nets with three layers. The first input layer feeds data to a hidden intermediate layer. The hidden layer processes the data and transports it to the output layer. Only the tap weights between the hidden layer and the output layer are modified during training. Each hidden layer neuron represents a basis function of the output space, with respect to a particular center in the input space. The activation function chosen is commonly a Gaussian kernel:

σ(ζ)=e−βζ2{\displaystyle \sigma (\zeta )=e^{-\beta \zeta ^{2}}}

This kernel is centered at the point in the input space specified by the weight vector. The closer the input signal is to the current weight vector, the higher the output of the neuron will be. Radial basis function networks are used commonly in function approximation and series prediction.